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Passing Ability Impact Factors Simulation of Articulated Vehicle with 6×4 Tractor

2015· article· en· W2025792062 on OpenAlexaff
H. Zhang, H.W. Zhang, J.Q. Yang

Bibliographic record

VenueApplied Mechanics and Materials · 2015
Typearticle
Languageen
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsMinistry of Transportation of Ontario
Fundersnot available
KeywordsTractorAxleAutomotive engineeringEngineeringArticulated vehicleSimulationMechanical engineering

Abstract

fetched live from OpenAlex

Passing ability of articulated vehicles is analyzed, which are combined of different 6×4 tractors and different semitrailers. According to the requirements of GB 1589, the passing ability of different structure articulated vehicles are simulated using AutoTURN. In order to analyze the passing ability impact factor of articulated vehicles in type and structure, simulation analysis is used to study how the tractor front overhang, the distance between center of tractor fifth wheel and the tractor first axle, and the semitrailer first wheel space impact on the passing ability. Reasonable suggestions are proposed by analyzing how the changes of parameters impact on the passing ability. The validity of the simulation model is validated by the test. The results serve as a useful reference for optimizing the articulated vehicle passing ability and promoting the development of vehicle industry technology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.295
Threshold uncertainty score0.330

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.029
GPT teacher head0.265
Teacher spread0.236 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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